This commit is contained in:
Guillem Borrell
parent
181bc92884
commit
98a713b3c7
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@ -19,7 +19,8 @@ You'll need the following environment variables in a .env file:
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* `GCS_ACCESS`
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* `GCS_SECRET`
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* `ANYSCALE_API_KEY`
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* `LLM_API_KEY`
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* `LLM_BASE_URL`
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* `GCS_BUCKETNAME`
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And to get the application up and running...
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140
notebooks/newtasks.ipynb
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140
notebooks/newtasks.ipynb
Normal file
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@ -0,0 +1,140 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"client = openai.OpenAI(\n",
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" base_url = \"https://api.fireworks.ai/inference/v1\",\n",
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" api_key = \"vQdRZPGX7Mvd9XEAIP8VAe5w1comMroY765vMfHW9rqbS48I\"\n",
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")\n",
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"\n",
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"messages = [\n",
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" {\"role\": \"system\", \"content\": f\"You are a helpful assistant with access to functions.\" \n",
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" \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Use them if required.\"},\n",
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" {\"role\": \"user\", \"content\": \"What are Nike's net income in 2022?\"}\n",
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"]\n",
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"\n",
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"tools = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" # name of the function \n",
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" \"name\": \"get_financial_data\",\n",
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" # a good, detailed description for what the function is supposed to do\n",
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" \"description\": \"Get financial data for a company given the metric and year.\",\n",
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" # a well defined json schema: https://json-schema.org/learn/getting-started-step-by-step#define\n",
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" \"parameters\": {\n",
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" # for OpenAI compatibility, we always declare a top level object for the parameters of the function\n",
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" \"type\": \"object\",\n",
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" # the properties for the object would be any arguments you want to provide to the function\n",
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" \"properties\": {\n",
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" \"metric\": {\n",
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" # JSON Schema supports string, number, integer, object, array, boolean and null\n",
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" # for more information, please check out https://json-schema.org/understanding-json-schema/reference/type\n",
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" \"type\": \"string\",\n",
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" # You can restrict the space of possible values in an JSON Schema\n",
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" # you can check out https://json-schema.org/understanding-json-schema/reference/enum for more examples on how enum works\n",
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" \"enum\": [\"net_income\", \"revenue\", \"ebdita\"],\n",
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" },\n",
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" \"financial_year\": {\n",
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" \"type\": \"integer\", \n",
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" # If the model does not understand how it is supposed to fill the field, a good description goes a long way \n",
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" \"description\": \"Year for which we want to get financial data.\"\n",
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" },\n",
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" \"company\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Name of the company for which we want to get financial data.\"\n",
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" }\n",
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" },\n",
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" # You can specify which of the properties from above are required\n",
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" # for more info on `required` field, please check https://json-schema.org/understanding-json-schema/reference/object#required\n",
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" \"required\": [\"metric\", \"financial_year\", \"company\"],\n",
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" },\n",
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" },\n",
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" }\n",
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"]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"content\": null,\n",
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" \"role\": \"assistant\",\n",
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" \"function_call\": null,\n",
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" \"tool_calls\": [\n",
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" {\n",
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" \"id\": \"call_W4IUqQRFF9vYINQ74tfBwmqr\",\n",
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" \"function\": {\n",
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" \"arguments\": \"{\\\"metric\\\": \\\"net_income\\\", \\\"financial_year\\\": 2022, \\\"company\\\": \\\"Nike\\\"}\",\n",
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" \"name\": \"get_financial_data\"\n",
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" },\n",
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" \"type\": \"function\",\n",
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" \"index\": 0\n",
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" }\n",
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" ]\n",
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"}\n"
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]
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}
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],
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"source": [
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"\n",
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"chat_completion = client.chat.completions.create(\n",
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" model=\"accounts/fireworks/models/firefunction-v2\",\n",
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" messages=messages,\n",
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" tools=tools,\n",
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" temperature=0.1\n",
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")\n",
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"print(chat_completion.choices[0].message.model_dump_json(indent=4))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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File diff suppressed because one or more lines are too long
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@ -1,5 +1,6 @@
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langchain
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langchain-community
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langchain-fireworks
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openai
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fastapi
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pydantic-settings
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@ -1,13 +1,15 @@
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# This file was autogenerated by uv via the following command:
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# uv pip compile requirements.in
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aiobotocore==2.13.0
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aiobotocore==2.13.1
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# via s3fs
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aiofiles==23.2.1
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aiofiles==24.1.0
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# via -r requirements.in
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aiohttp==3.9.5
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# via
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# aiobotocore
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# langchain
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# langchain-community
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# langchain-fireworks
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# s3fs
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aioitertools==0.11.0
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# via aiobotocore
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@ -24,9 +26,10 @@ anyio==4.4.0
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attrs==23.2.0
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# via aiohttp
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authlib==1.3.1
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botocore==1.34.106
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# via -r requirements.in
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botocore==1.34.131
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# via aiobotocore
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certifi==2024.6.2
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certifi==2024.7.4
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# via
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# httpcore
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# httpx
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@ -50,16 +53,24 @@ distro==1.9.0
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dnspython==2.6.1
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# via email-validator
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duckdb==1.0.0
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email-validator==2.1.1
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# via
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# -r requirements.in
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# duckdb-engine
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duckdb-engine==0.13.0
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# via -r requirements.in
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email-validator==2.2.0
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# via fastapi
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fastapi==0.111.0
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# via -r requirements.in
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fastapi-cli==0.0.4
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# via fastapi
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fireworks-ai==0.14.0
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# via langchain-fireworks
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frozenlist==1.4.1
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# via
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# aiohttp
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# aiosignal
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fsspec==2024.6.0
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fsspec==2024.6.1
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# via s3fs
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h11==0.14.0
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# via
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@ -72,7 +83,10 @@ httptools==0.6.1
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httpx==0.27.0
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# via
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# fastapi
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# fireworks-ai
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# openai
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httpx-sse==0.4.0
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# via fireworks-ai
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idna==3.7
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# via
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# anyio
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@ -81,6 +95,7 @@ idna==3.7
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# requests
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# yarl
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itsdangerous==2.2.0
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# via -r requirements.in
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jinja2==3.1.4
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# via fastapi
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jmespath==1.0.1
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@ -89,17 +104,23 @@ jsonpatch==1.33
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# via langchain-core
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jsonpointer==3.0.0
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# via jsonpatch
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langchain==0.2.3
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# via langchain-community
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langchain-community==0.2.4
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langchain-core==0.2.5
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langchain==0.2.7
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# via
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# -r requirements.in
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# langchain-community
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langchain-community==0.2.7
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# via -r requirements.in
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langchain-core==0.2.17
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# via
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# langchain
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# langchain-community
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# langchain-fireworks
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# langchain-text-splitters
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langchain-text-splitters==0.2.1
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langchain-fireworks==0.1.5
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# via -r requirements.in
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langchain-text-splitters==0.2.2
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# via langchain
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langsmith==0.1.76
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langsmith==0.1.85
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# via
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# langchain
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# langchain-community
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@ -123,33 +144,44 @@ numpy==1.26.4
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# langchain
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# langchain-community
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# pyarrow
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openai==1.33.0
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orjson==3.10.4
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openai==1.35.13
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# via
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# -r requirements.in
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# langchain-fireworks
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orjson==3.10.6
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# via
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# fastapi
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# langsmith
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packaging==23.2
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packaging==24.1
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# via
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# duckdb-engine
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# langchain-core
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# marshmallow
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polars==0.20.31
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pillow==10.4.0
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# via fireworks-ai
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polars==1.1.0
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# via -r requirements.in
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pyarrow==16.1.0
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# via -r requirements.in
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pycparser==2.22
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# via cffi
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pydantic==2.7.3
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pydantic==2.8.2
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# via
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# fastapi
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# fireworks-ai
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# langchain
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# langchain-core
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# langsmith
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# openai
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# pydantic-settings
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pydantic-core==2.18.4
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pydantic-core==2.20.1
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# via pydantic
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pydantic-settings==2.3.2
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pydantic-settings==2.3.4
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# via -r requirements.in
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pygments==2.18.0
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# via rich
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pyjwt==2.8.0
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# via -r requirements.in
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python-dateutil==2.9.0.post0
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# via botocore
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python-dotenv==1.0.1
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|
@ -157,7 +189,9 @@ python-dotenv==1.0.1
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# pydantic-settings
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# uvicorn
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python-multipart==0.0.9
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# via fastapi
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# via
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# -r requirements.in
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# fastapi
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||||
pyyaml==6.0.1
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# via
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# langchain
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|
|
@ -168,10 +202,12 @@ requests==2.32.3
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# via
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# langchain
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# langchain-community
|
||||
# langchain-fireworks
|
||||
# langsmith
|
||||
rich==13.7.1
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# via typer
|
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s3fs==2024.6.0
|
||||
s3fs==2024.6.1
|
||||
# via -r requirements.in
|
||||
shellingham==1.5.4
|
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# via typer
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six==1.16.0
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|
@ -181,13 +217,15 @@ sniffio==1.3.1
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# anyio
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# httpx
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# openai
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sqlalchemy==2.0.30
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sqlalchemy==2.0.31
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# via
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# -r requirements.in
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# duckdb-engine
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# langchain
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||||
# langchain-community
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starlette==0.37.2
|
||||
# via fastapi
|
||||
tenacity==8.3.0
|
||||
tenacity==8.5.0
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||||
# via
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# langchain
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||||
# langchain-community
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||||
|
|
@ -209,7 +247,7 @@ typing-inspect==0.9.0
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# via dataclasses-json
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ujson==5.10.0
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||||
# via fastapi
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||||
urllib3==2.2.1
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||||
urllib3==2.2.2
|
||||
# via
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# botocore
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# requests
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|
|
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@ -3,7 +3,8 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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base_url: str = "http://localhost:8000"
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anyscale_api_key: str = "Awesome API"
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llm_api_key: str = "Awesome API"
|
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llm_base_url: str = "Awessome Endpoint"
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gcs_access: str = "access"
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gcs_secret: str = "secret"
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||||
gcs_bucketname: str = "bucket"
|
||||
|
|
|
|||
|
|
@ -22,7 +22,6 @@ app.add_middleware(SessionMiddleware, secret_key=settings.app_secret_key)
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async def homepage(request: Request):
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||||
user = request.session.get("user")
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||||
if user:
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||||
print(json.dumps(user))
|
||||
return RedirectResponse("/app")
|
||||
|
||||
with open(static_path / "login.html") as f:
|
||||
|
|
|
|||
|
|
@ -1,14 +1,34 @@
|
|||
from enum import StrEnum
|
||||
|
||||
from langchain_community.chat_models import ChatAnyscale
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
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from langchain_fireworks import Fireworks
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from langchain_core.prompts import PromptTemplate
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||||
|
||||
|
||||
class AvailableModels(StrEnum):
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llama3_8b = "meta-llama/Meta-Llama-3-8B-Instruct"
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llama3_70b = "meta-llama/Meta-Llama-3-70B-Instruct"
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||||
llama3_70b = "accounts/fireworks/models/llama-v3-70b-instruct"
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# Function calling model
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mixtral_8x7b = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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mixtral_8x7b = "accounts/fireworks/models/mixtral-8x7b-instruct"
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||||
mixtral_8x22b = "accounts/fireworks/models/mixtral-8x22b-instruct"
|
||||
firefunction_2 = "accounts/fireworks/models/firefunction-v2"
|
||||
|
||||
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||||
general_prompt = """
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||||
You're a helpful assistant. Perform the following tasks:
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||||
|
||||
----
|
||||
{query}
|
||||
----
|
||||
"""
|
||||
|
||||
sql_prompt = """
|
||||
You're a SQL expert. Write a query using the duckdb dialect. The goal of the query is the following:
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||||
|
||||
----
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||||
{query}
|
||||
----
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||||
|
||||
Return only the sql statement without any additional text.
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||||
"""
|
||||
|
||||
|
||||
class Chat:
|
||||
|
|
@ -25,33 +45,36 @@ class Chat:
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|||
|
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def __init__(
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self,
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||||
model: AvailableModels = AvailableModels.llama3_8b,
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model: AvailableModels = AvailableModels.mixtral_8x7b,
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api_key: str = "",
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temperature: float = 0.5,
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||||
):
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self.model_name = model
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||||
self.model = model
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||||
self.api_key = self.raise_no_key(api_key)
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self.messages = []
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self.responses = []
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|
||||
self.model: ChatAnyscale = ChatAnyscale(
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model_name=model, temperature=temperature, anyscale_api_key=self.api_key
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||||
self.model: Fireworks = Fireworks(
|
||||
model=model, temperature=temperature, api_key=self.api_key
|
||||
)
|
||||
|
||||
async def eval(self, system: str, human: str):
|
||||
self.messages.append(
|
||||
[
|
||||
SystemMessage(content=system),
|
||||
HumanMessage(content=human),
|
||||
]
|
||||
)
|
||||
async def eval(self, task):
|
||||
prompt = PromptTemplate.from_template(general_prompt)
|
||||
|
||||
response = await self.model.ainvoke(self.messages[-1])
|
||||
response = await self.model.ainvoke(prompt.format(query=task))
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||||
self.responses.append(response)
|
||||
return self
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||||
|
||||
async def sql_eval(self, question):
|
||||
prompt = PromptTemplate.from_template(sql_prompt)
|
||||
|
||||
response = await self.model.ainvoke(prompt.format(query=question))
|
||||
self.responses.append(response)
|
||||
return self
|
||||
|
||||
def last_response_content(self):
|
||||
return self.responses[-1].content
|
||||
last_response = self.responses[-1]
|
||||
return last_response
|
||||
|
||||
def last_response_metadata(self):
|
||||
return self.responses[-1].response_metadata
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ router = APIRouter()
|
|||
|
||||
@router.get("/query", response_class=PlainTextResponse, tags=["queries"])
|
||||
async def query(sid: str = "", q: str = "") -> str:
|
||||
llm = Chat(api_key=settings.anyscale_api_key, temperature=0.5)
|
||||
llm = Chat(api_key=settings.llm_api_key, temperature=0.5)
|
||||
db = SessionDB(
|
||||
StorageEngines.gcs,
|
||||
gcs_access=settings.gcs_access,
|
||||
|
|
|
|||
|
|
@ -20,45 +20,45 @@ SID = "test"
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.skipif(
|
||||
settings.anyscale_api_key == "Awesome API", reason="API Key not set"
|
||||
)
|
||||
@pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
|
||||
async def test_chat_simple():
|
||||
chat = Chat(api_key=settings.anyscale_api_key, temperature=0)
|
||||
chat = await chat.eval("Your're a helpful assistant", "Say literlly 'Hello'")
|
||||
assert chat.last_response_content() == "Hello!"
|
||||
chat = Chat(api_key=settings.llm_api_key, temperature=0)
|
||||
chat = await chat.eval("Say literlly 'Hello'")
|
||||
assert "Hello" in chat.last_response_content()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.skipif(
|
||||
settings.anyscale_api_key == "Awesome API", reason="API Key not set"
|
||||
)
|
||||
@pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
|
||||
async def test_simple_data_query():
|
||||
query = "write a query that finds the average score of all students in the current database"
|
||||
|
||||
chat = Chat(api_key=settings.anyscale_api_key, temperature=0.5)
|
||||
chat = Chat(
|
||||
api_key=settings.llm_api_key,
|
||||
temperature=0.5,
|
||||
)
|
||||
db = SessionDB(
|
||||
storage_engine=StorageEngines.local, path=TEST_XLS_PATH.parent, sid=SID
|
||||
).load_xls(TEST_XLS_PATH)
|
||||
|
||||
chat = await chat.eval("You're an expert sql developer", db.query_prompt(query))
|
||||
chat = await chat.sql_eval(db.query_prompt(query))
|
||||
query = extract_code_block(chat.last_response_content())
|
||||
assert query.startswith("SELECT")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.skipif(
|
||||
settings.anyscale_api_key == "Awesome API", reason="API Key not set"
|
||||
)
|
||||
@pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
|
||||
async def test_data_query():
|
||||
q = "find the average score of all the sudents"
|
||||
|
||||
llm = Chat(api_key=settings.anyscale_api_key, temperature=0.5)
|
||||
llm = Chat(
|
||||
api_key=settings.llm_api_key,
|
||||
temperature=0.5,
|
||||
)
|
||||
db = SessionDB(
|
||||
storage_engine=TEST_STORAGE, path=TEST_OUTPUT_FOLDER, sid="test"
|
||||
).load_folder()
|
||||
|
||||
chat = await llm.eval("You're a DUCKDB expert", db.query_prompt(q))
|
||||
chat = await llm.sql_eval(db.query_prompt(q))
|
||||
query = extract_code_block(chat.last_response_content())
|
||||
result = db.query(query).pl()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue